Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

Autor: Olesen, Alexander Neergaard, Jennum, Poul, Peppard, Paul, Mignot, Emmanuel, Sorensen, Helge Bjarup Dissing

Publikováno v: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 1-4

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of fiv

Externí odkaz: http://arxiv.org/abs/1810.03745

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Channel selection for automatic seizure detection

Autor: Duun-Henriksen, Jonas, Kjaer, Troels Wesenberg, Madsen, Rasmus Elsborg, Remvig, Line Sofie, Thomsen, Carsten Eckhart, Sorensen, Helge Bjarup Dissing

Publikováno v: In Clinical Neurophysiology 2012 123(1):84-92

Robust, ECG-based algorithm for Sleep Disordered Breathing detection in large population-based cohorts using an automatic, data-driven approach

Autor: Olsen, Mads, Mignot, Emmanuel, Jennum, Poul J., Sørensen, Helge Bjarup Dissing

Publikováno v: Olsen, M, Mignot, E, Jennum, P J & Sørensen, H B D 2020, ' Robust, ECG-based algorithm for Sleep Disordered Breathing detection in large population-based cohorts using an automatic, data-driven approach ', Sleep, vol. 43, no. 5, zsz276 . https://doi.org/10.1093/sleep/zsz276

Up to 5% of adults in Western countries have undiagnosed sleep disordered breathing (SDB). Studies have shown that electrocardiogram (ECG)-based algorithms can identify SDB and may provide alternative screening. Most studies however have limited gene

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1202::a2f4941edfedce439e95a566b42627fb
https://orbit.dtu.dk/en/publications/ad40bd51-5aa9-45ea-bc6a-df4e47c9ae88

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